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Matthew Honnibal
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13
spacy/syntax/_neural.pxd
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13
spacy/syntax/_neural.pxd
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from thinc.linear.avgtron cimport AveragedPerceptron
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from thinc.neural.nn cimport NeuralNet
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from thinc.linear.features cimport ConjunctionExtracter
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from thinc.structs cimport NeuralNetC, ExampleC
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cdef class ParserNeuralNet(NeuralNet):
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cdef ConjunctionExtracter extracter
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cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil
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cdef class ParserPerceptron(AveragedPerceptron):
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cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil
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189
spacy/syntax/_neural.pyx
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spacy/syntax/_neural.pyx
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# cython: infer_types=True
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# cython: profile=True
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from libc.stdint cimport uint64_t
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from libc.string cimport memcpy, memset
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from cymem.cymem cimport Pool, Address
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from murmurhash.mrmr cimport hash64
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from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t, idx_t
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from thinc.linear.avgtron cimport AveragedPerceptron
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from thinc.linalg cimport VecVec
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from thinc.structs cimport NeuralNetC, SparseArrayC, ExampleC
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from thinc.structs cimport FeatureC
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from thinc.extra.eg cimport Example
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from preshed.maps cimport map_get
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from preshed.maps cimport MapStruct
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from ..structs cimport TokenC
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from ._state cimport StateC
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from ._parse_features cimport fill_context
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from ._parse_features cimport CONTEXT_SIZE
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from ._parse_features cimport fill_context
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from ._parse_features cimport *
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cdef class ParserPerceptron(AveragedPerceptron):
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@property
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def widths(self):
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return (self.extracter.nr_templ,)
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def update(self, Example eg):
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'''Does regression on negative cost. Sort of cute?'''
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self.time += 1
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cdef weight_t loss = 0.0
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best = eg.best
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for clas in range(eg.c.nr_class):
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if not eg.c.is_valid[clas]:
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continue
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if eg.c.scores[clas] < eg.c.scores[best]:
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continue
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loss += (-eg.c.costs[clas] - eg.c.scores[clas]) ** 2
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d_loss = 2 * (-eg.c.costs[clas] - eg.c.scores[clas])
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step = d_loss * 0.001
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for feat in eg.c.features[:eg.c.nr_feat]:
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self.update_weight(feat.key, clas, feat.value * step)
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return int(loss)
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cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil:
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state = <const StateC*>_state
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fill_context(eg.atoms, state)
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eg.nr_feat = self.extracter.set_features(eg.features, eg.atoms)
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cdef class ParserNeuralNet(NeuralNet):
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def __init__(self, shape, **kwargs):
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vector_widths = [4] * 76
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slots = [0, 1, 2, 3] # S0
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slots += [4, 5, 6, 7] # S1
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slots += [8, 9, 10, 11] # S2
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slots += [12, 13, 14, 15] # S3+
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slots += [16, 17, 18, 19] # B0
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slots += [20, 21, 22, 23] # B1
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slots += [24, 25, 26, 27] # B2
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slots += [28, 29, 30, 31] # B3+
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slots += [32, 33, 34, 35] * 2 # S0l, S0r
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slots += [36, 37, 38, 39] * 2 # B0l, B0r
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slots += [40, 41, 42, 43] * 2 # S1l, S1r
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slots += [44, 45, 46, 47] * 2 # S2l, S2r
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slots += [48, 49, 50, 51, 52, 53, 54, 55]
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slots += [53, 54, 55, 56]
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input_length = sum(vector_widths[slot] for slot in slots)
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widths = [input_length] + shape
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NeuralNet.__init__(self, widths, embed=(vector_widths, slots), **kwargs)
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@property
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def nr_feat(self):
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return 2000
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cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil:
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memset(eg.features, 0, 2000 * sizeof(FeatureC))
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state = <const StateC*>_state
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fill_context(eg.atoms, state)
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feats = eg.features
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feats = _add_token(feats, 0, state.S_(0), 1.0)
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feats = _add_token(feats, 4, state.S_(1), 1.0)
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feats = _add_token(feats, 8, state.S_(2), 1.0)
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# Rest of the stack, with exponential decay
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for i in range(3, state.stack_depth()):
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feats = _add_token(feats, 12, state.S_(i), 1.0 * 0.5**(i-2))
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feats = _add_token(feats, 16, state.B_(0), 1.0)
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feats = _add_token(feats, 20, state.B_(1), 1.0)
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feats = _add_token(feats, 24, state.B_(2), 1.0)
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# Rest of the buffer, with exponential decay
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for i in range(3, min(8, state.buffer_length())):
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feats = _add_token(feats, 28, state.B_(i), 1.0 * 0.5**(i-2))
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feats = _add_subtree(feats, 32, state, state.S(0))
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feats = _add_subtree(feats, 40, state, state.B(0))
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feats = _add_subtree(feats, 48, state, state.S(1))
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feats = _add_subtree(feats, 56, state, state.S(2))
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feats = _add_pos_bigram(feats, 64, state.S_(0), state.B_(0))
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feats = _add_pos_bigram(feats, 65, state.S_(1), state.S_(0))
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feats = _add_pos_bigram(feats, 66, state.S_(1), state.B_(0))
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feats = _add_pos_bigram(feats, 67, state.S_(0), state.B_(1))
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feats = _add_pos_bigram(feats, 68, state.S_(0), state.R_(state.S(0), 1))
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feats = _add_pos_bigram(feats, 69, state.S_(0), state.R_(state.S(0), 2))
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feats = _add_pos_bigram(feats, 70, state.S_(0), state.L_(state.S(0), 1))
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feats = _add_pos_bigram(feats, 71, state.S_(0), state.L_(state.S(0), 2))
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feats = _add_pos_trigram(feats, 72, state.S_(1), state.S_(0), state.B_(0))
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feats = _add_pos_trigram(feats, 73, state.S_(0), state.B_(0), state.B_(1))
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feats = _add_pos_trigram(feats, 74, state.S_(0), state.R_(state.S(0), 1),
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state.R_(state.S(0), 2))
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feats = _add_pos_trigram(feats, 75, state.S_(0), state.L_(state.S(0), 1),
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state.L_(state.S(0), 2))
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eg.nr_feat = feats - eg.features
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cdef void _set_delta_lossC(self, weight_t* delta_loss,
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const weight_t* cost, const weight_t* scores) nogil:
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for i in range(self.c.widths[self.c.nr_layer-1]):
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delta_loss[i] = cost[i]
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cdef void _softmaxC(self, weight_t* out) nogil:
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pass
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cdef inline FeatureC* _add_token(FeatureC* feats,
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int slot, const TokenC* token, weight_t value) nogil:
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# Word
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feats.i = slot
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feats.key = token.lex.norm
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feats.value = value
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feats += 1
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# POS tag
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feats.i = slot+1
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feats.key = token.tag
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feats.value = value
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feats += 1
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# Dependency label
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feats.i = slot+2
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feats.key = token.dep
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feats.value = value
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feats += 1
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# Word, label, tag
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feats.i = slot+3
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cdef uint64_t key[3]
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key[0] = token.lex.cluster
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key[1] = token.tag
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key[2] = token.dep
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feats.key = hash64(key, sizeof(key), 0)
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feats.value = value
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feats += 1
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return feats
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cdef inline FeatureC* _add_subtree(FeatureC* feats, int slot, const StateC* state, int t) nogil:
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value = 1.0
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for i in range(state.n_R(t)):
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feats = _add_token(feats, slot, state.R_(t, i+1), value)
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value *= 0.5
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slot += 4
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value = 1.0
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for i in range(state.n_L(t)):
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feats = _add_token(feats, slot, state.L_(t, i+1), value)
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value *= 0.5
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return feats
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cdef inline FeatureC* _add_pos_bigram(FeatureC* feat, int slot,
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const TokenC* t1, const TokenC* t2) nogil:
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cdef uint64_t[2] key
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key[0] = t1.tag
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key[1] = t2.tag
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feat.i = slot
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feat.key = hash64(key, sizeof(key), slot)
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feat.value = 1.0
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return feat+1
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cdef inline FeatureC* _add_pos_trigram(FeatureC* feat, int slot,
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const TokenC* t1, const TokenC* t2, const TokenC* t3) nogil:
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cdef uint64_t[3] key
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key[0] = t1.tag
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key[1] = t2.tag
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key[2] = t3.tag
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feat.i = slot
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feat.key = hash64(key, sizeof(key), slot)
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feat.value = 1.0
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return feat+1
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